Changing landscape of nursing homes in Singapore: challenges in the 21st century

The ageing population is posing new challenges to Singapore's healthcare system. The rise of dual income and the decline of extended families, as well as an increase in age-related degenerative disorders due to increased longevity render it difficult for the family to remain the primary social safety net to care for our elderly in their own homes. Consequently, nursing homes may become increasingly relevant for resource and expertise-challenged families to cope with the burden of caring for a frail and dependent elderly. However, as the recent Nightingale Nursing Home elderly mistreatment incident attests, the standards of some have been found wanting. This paper will trace the history of nursing homes in Singapore and the evolution of government policies towards them, discuss the challenges and trade-offs of nursing home regulation, and provide suggestions for better care and governance.

Factors affecting unplanned readmissions from community hospitals to acute hospitals: a prospective observational study

<p><b>INTRODUCTION</b>While the readmission rate from community hospitals is known, the factors affecting it are not. Our aim was to determine the factors predicting unplanned readmissions from community hospitals (CHs) to acute hospitals (AHs).</p><p><b>MATERIALS AND METHODS</b>This was an observational prospective cohort study, involving 842 patients requiring post-acute rehabilitation in 2 CHs admitted from 3 AHs in Singapore. We studied the role of the Cumulative Illness Rating Scale (CIRS) organ impairment scores, the Mini-mental State Examination (MMSE) score, the Shah modified Barthel Index (BI) score, and the triceps skin fold thickness (TSFT) in predicting the rate of unplanned readmissions (UR), early unplanned readmissions (EUPR) and late unplanned readmissions (LUPR). We developed a clinical prediction rule to determine the risk of UR and EUPR.</p><p><b>RESULTS</b>The rates of EUPR and LUPR were 7.6% and 10.3% respectively. The factors that predicted UR were the CIRS-heart score, the CIRS-haemopoietic score, the CIRS-endocrine / metabolic score and the BI on admission. The MMSE was predictive of EUPR. The TSFT and CIRS-liver score were predictive of LUPR. Upon receiver operator characteristics analysis, the clinical prediction rules for the prediction of EUPR and UR had areas under the curve of 0.745 and 0.733 respectively. The likelihood ratios of the clinical prediction rules for EUPR and UR ranged from 0.42 to 5.69 and 0.34 to 3.16 respectively.</p><p><b>CONCLUSIONS</b>Patients who have UR can be identified by the admission BI, the MMSE, the TSFT and CIRS scores in the cardiac, haemopoietic, liver and endocrine/metabolic systems.</p>